Automated Recognition of Railroad Infrastructure in Rural Areas from LIDAR Data

This study is aimed at developing automated methods to recognize railroad infrastructure from 3D LIDAR data. Railroad infrastructure includes rail tracks, contact cables, catenary cables, return current cables, masts, and cantilevers. The LIDAR dataset used in this study is acquired by placing an Optech Lynx mobile mapping system on a railcar, operating at 125 km/h. The acquired dataset covers 550 meters of Austrian rural railroad corridor comprising 31 railroad key elements and containing only spatial information. The proposed methodology recognizes key components of the railroad corridor based on their physical shape, geometrical properties, and the topological relationships among them. The developed algorithms managed to recognize all key components of the railroad infrastructure, including two rail tracks, thirteen masts, thirteen cantilevers, one contact cable, one catenary cable, and one return current cable. The results are presented and discussed both at object level and at point cloud level. The results indicate that 100% accuracy and 100% precision at the object level and an average of 96.4% accuracy and an average of 97.1% precision at point cloud level are achieved.